2 research outputs found
Learning from Multiway Data: Simple and Efficient Tensor Regression
Tensor regression has shown to be advantageous in learning tasks with
multi-directional relatedness. Given massive multiway data, traditional methods
are often too slow to operate on or suffer from memory bottleneck. In this
paper, we introduce subsampled tensor projected gradient to solve the problem.
Our algorithm is impressively simple and efficient. It is built upon projected
gradient method with fast tensor power iterations, leveraging randomized
sketching for further acceleration. Theoretical analysis shows that our
algorithm converges to the correct solution in fixed number of iterations. The
memory requirement grows linearly with the size of the problem. We demonstrate
superior empirical performance on both multi-linear multi-task learning and
spatio-temporal applications.Comment: 10 pages, Proceedings of the 33rd International Conference on Machine
Learning (ICML-16), 201
Spatio-Temporal Data Mining: A Survey of Problems and Methods
Large volumes of spatio-temporal data are increasingly collected and studied
in diverse domains including, climate science, social sciences, neuroscience,
epidemiology, transportation, mobile health, and Earth sciences.
Spatio-temporal data differs from relational data for which computational
approaches are developed in the data mining community for multiple decades, in
that both spatial and temporal attributes are available in addition to the
actual measurements/attributes. The presence of these attributes introduces
additional challenges that needs to be dealt with. Approaches for mining
spatio-temporal data have been studied for over a decade in the data mining
community. In this article we present a broad survey of this relatively young
field of spatio-temporal data mining. We discuss different types of
spatio-temporal data and the relevant data mining questions that arise in the
context of analyzing each of these datasets. Based on the nature of the data
mining problem studied, we classify literature on spatio-temporal data mining
into six major categories: clustering, predictive learning, change detection,
frequent pattern mining, anomaly detection, and relationship mining. We discuss
the various forms of spatio-temporal data mining problems in each of these
categories.Comment: Accepted for publication at ACM Computing Survey